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Apache Spark 2.x Cookbook

You're reading from   Apache Spark 2.x Cookbook Over 70 cloud-ready recipes for distributed Big Data processing and analytics

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Product type Paperback
Published in May 2017
Publisher
ISBN-13 9781787127265
Length 294 pages
Edition 1st Edition
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Author (1):
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Rishi Yadav Rishi Yadav
Author Profile Icon Rishi Yadav
Rishi Yadav
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Apache Spark FREE CHAPTER 2. Developing Applications with Spark 3. Spark SQL 4. Working with External Data Sources 5. Spark Streaming 6. Getting Started with Machine Learning 7. Supervised Learning with MLlib — Regression 8. Supervised Learning with MLlib — Classification 9. Unsupervised Learning 10. Recommendations Using Collaborative Filtering 11. Graph Processing Using GraphX and GraphFrames 12. Optimizations and Performance Tuning

Creating vectors


Before understanding vectors, let's focus on what a point is. A point is just a set of numbers. This set of numbers or coordinates defines the point's position in space. The number of coordinates determines the dimensions of the space.

We can visualize space with up to three dimensions. A space with more than three dimensions is called hyperspace. Let's put this spatial metaphor to use.

Getting ready

Let's start with a house. A house may have the following dimensions:

  • Area
  • Lot size
  • Number of rooms

We are working in three-dimensional space here. Thus, the interpretation of point (4500, 41000, 4) would be 4500 sq. ft area, 41k sq. ft lot size, and four rooms.

Points and vectors are the same thing. Dimensions in vectors are called features. In another way, we can define a feature as an individual measurable property of a phenomenon being observed.

Spark has local vectors and matrices and also distributed matrices. A distributed matrix is backed by one or more RDDs. A local vector has...

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